def wrapper(self, other):
op = getattr(self._ndarray_values, opname)
if isinstance(other, Period):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other.ordinal)
elif isinstance(other, PeriodArrayMixin):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other._ndarray_values)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self._ndarray_values), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
def _add_offset(self, other):
assert not isinstance(other, Tick)
base = frequencies.get_base_alias(other.rule_code)
if base != self.freq.rule_code:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
return self.shift(other.n)
def __init__(self, values, freq=None, dtype=None, copy=False):
freq = dtl.validate_dtype_freq(dtype, freq)
if freq is not None:
freq = Period._maybe_convert_freq(freq)
if isinstance(values, ABCSeries):
values = values._values
if not isinstance(values, type(self)):
raise TypeError("Incorrect dtype")
elif isinstance(values, ABCPeriodIndex):
values = values._values
if isinstance(values, type(self)):
if freq is not None and freq != values.freq:
msg = DIFFERENT_FREQ_INDEX.format(values.freq.freqstr,
freq.freqstr)
raise IncompatibleFrequency(msg)
values, freq = values._data, values.freq
values = np.array(values, dtype='int64', copy=copy)
self._data = values
if freq is None:
raise ValueError('freq is not specified and cannot be inferred')
self._dtype = PeriodDtype(freq)
def _assert_can_do_setop(self, other):
super(PeriodIndex, self)._assert_can_do_setop(other)
if not isinstance(other, PeriodIndex):
raise ValueError('can only call with other PeriodIndex-ed objects')
if self.freq != other.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
def __setitem__(
self,
key, # type: Union[int, Sequence[int], Sequence[bool], slice]
value # type: Union[NaTType, Period, Sequence[Period]]
):
# type: (...) -> None
# n.b. the type on `value` is a bit too restrictive.
# we also accept a sequence of stuff coercible to a PeriodArray
# by period_array, which includes things like ndarray[object],
# ndarray[datetime64ns]. I think ndarray[int] / ndarray[str] won't
# work, since the freq can't be inferred.
if is_list_like(value):
is_slice = isinstance(key, slice)
if (not is_slice
and len(key) != len(value)
and not com.is_bool_indexer(key)):
msg = ("shape mismatch: value array of length '{}' does not "
"match indexing result of length '{}'.")
raise ValueError(msg.format(len(key), len(value)))
if not is_slice and len(key) == 0:
return
value = period_array(value)
if self.freqstr != value.freqstr:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
raise IncompatibleFrequency(msg)
value = value.asi8
elif isinstance(value, Period):
if self.freqstr != value.freqstr:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
raise IncompatibleFrequency(msg)
value = value.ordinal
elif isna(value):
value = iNaT
else:
msg = ("'value' should be a 'Period', 'NaT', or array of those. "
"Got '{}' instead.".format(type(value).__name__))
raise TypeError(msg)
self._data[key] = value
def searchsorted(self, value, side='left', sorter=None):
if isinstance(value, Period):
if value.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
raise IncompatibleFrequency(msg)
value = value.ordinal
elif isinstance(value, compat.string_types):
value = Period(value, freq=self.freq).ordinal
return self._ndarray_values.searchsorted(value, side=side,
sorter=sorter)
def wrapper(self, other):
op = getattr(self.asi8, opname)
# We want to eventually defer to the Series or PeriodIndex (which will
# return here with an unboxed PeriodArray). But before we do that,
# we do a bit of validation on type (Period) and freq, so that our
# error messages are sensible
not_implemented = isinstance(other, (ABCSeries, ABCIndexClass))
if not_implemented:
other = other._values
if isinstance(other, Period):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
result = op(other.ordinal)
elif isinstance(other, cls):
if other.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
if not_implemented:
return NotImplemented
result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self.hasnans:
result[self._isnan] = nat_result
return result
def _add_offset(self, other):
assert not isinstance(other, Tick)
base = frequencies.get_base_alias(other.rule_code)
if base != self.freq.rule_code:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
# Note: when calling parent class's _add_timedeltalike_scalar,
# it will call delta_to_nanoseconds(delta). Because delta here
# is an integer, delta_to_nanoseconds will return it unchanged.
result = super(PeriodArray, self)._add_timedeltalike_scalar(other.n)
return type(self)(result, freq=self.freq)
def _validate_fill_value(self, fill_value):
if isna(fill_value):
fill_value = iNaT
elif isinstance(fill_value, Period):
if fill_value.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freq.freqstr,
fill_value.freqstr)
raise IncompatibleFrequency(msg)
fill_value = fill_value.ordinal
else:
raise ValueError("'fill_value' should be a Period. "
"Got '{got}'.".format(got=fill_value))
return fill_value
def searchsorted(self, value, side='left', sorter=None):
if isinstance(value, Period):
if value.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
raise IncompatibleFrequency(msg)
value = value.ordinal
elif isinstance(value, compat.string_types):
try:
value = Period(value, freq=self.freq).ordinal
except DateParseError:
raise KeyError("Cannot interpret '{}' as period".format(value))
return self._ndarray_values.searchsorted(value, side=side,
sorter=sorter)
def get_indexer(self, target, method=None, limit=None, tolerance=None):
target = ensure_index(target)
if hasattr(target, 'freq') and target.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, target.freqstr)
raise IncompatibleFrequency(msg)
if isinstance(target, PeriodIndex):
target = target.asi8
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
return Index.get_indexer(self._int64index, target, method,
limit, tolerance)
def get_indexer(self, target, method=None, limit=None, tolerance=None):
target = _ensure_index(target)
if hasattr(target, 'freq') and target.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, target.freqstr)
raise IncompatibleFrequency(msg)
if isinstance(target, PeriodIndex):
target = target.asi8
if tolerance is not None:
tolerance = self._convert_tolerance(tolerance, target)
return Index.get_indexer(self._int64index, target, method, limit,
tolerance)
def searchsorted(self, value, side='left', sorter=None):
if isinstance(value, Period):
if value.freq != self.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, value.freqstr)
raise IncompatibleFrequency(msg)
value = value.ordinal
elif isinstance(value, compat.string_types):
try:
value = Period(value, freq=self.freq).ordinal
except DateParseError:
raise KeyError("Cannot interpret '{}' as period".format(value))
return self._ndarray_values.searchsorted(value, side=side,
sorter=sorter)
def _sub_period(self, other):
# If the operation is well-defined, we return an object-Index
# of DateOffsets. Null entries are filled with pd.NaT
if self.freq != other.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
asi8 = self.asi8
new_data = asi8 - other.ordinal
new_data = np.array([self.freq * x for x in new_data])
if self.hasnans:
new_data[self._isnan] = NaT
return new_data
def _maybe_convert_timedelta(self, other):
"""
Convert timedelta-like input to an integer multiple of self.freq
Parameters
----------
other : timedelta, np.timedelta64, DateOffset, int, np.ndarray
Returns
-------
converted : int, np.ndarray[int64]
Raises
------
IncompatibleFrequency : if the input cannot be written as a multiple
of self.freq. Note IncompatibleFrequency subclasses ValueError.
"""
if isinstance(
other, (timedelta, np.timedelta64, Tick, np.ndarray)):
offset = frequencies.to_offset(self.freq.rule_code)
if isinstance(offset, Tick):
if isinstance(other, np.ndarray):
nanos = np.vectorize(delta_to_nanoseconds)(other)
else:
nanos = delta_to_nanoseconds(other)
offset_nanos = delta_to_nanoseconds(offset)
check = np.all(nanos % offset_nanos == 0)
if check:
return nanos // offset_nanos
elif isinstance(other, DateOffset):
freqstr = other.rule_code
base = frequencies.get_base_alias(freqstr)
if base == self.freq.rule_code:
return other.n
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
elif lib.is_integer(other):
# integer is passed to .shift via
# _add_datetimelike_methods basically
# but ufunc may pass integer to _add_delta
return other
# raise when input doesn't have freq
msg = "Input has different freq from {cls}(freq={freqstr})"
raise IncompatibleFrequency(msg.format(cls=type(self).__name__,
freqstr=self.freqstr))
def _maybe_convert_timedelta(self, other):
"""
Convert timedelta-like input to an integer multiple of self.freq
Parameters
----------
other : timedelta, np.timedelta64, DateOffset, int, np.ndarray
Returns
-------
converted : int, np.ndarray[int64]
Raises
------
IncompatibleFrequency : if the input cannot be written as a multiple
of self.freq. Note IncompatibleFrequency subclasses ValueError.
"""
if isinstance(
other, (timedelta, np.timedelta64, Tick, np.ndarray)):
offset = frequencies.to_offset(self.freq.rule_code)
if isinstance(offset, Tick):
if isinstance(other, np.ndarray):
nanos = np.vectorize(delta_to_nanoseconds)(other)
else:
nanos = delta_to_nanoseconds(other)
offset_nanos = delta_to_nanoseconds(offset)
check = np.all(nanos % offset_nanos == 0)
if check:
return nanos // offset_nanos
elif isinstance(other, DateOffset):
freqstr = other.rule_code
base = frequencies.get_base_alias(freqstr)
if base == self.freq.rule_code:
return other.n
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
elif lib.is_integer(other):
# integer is passed to .shift via
# _add_datetimelike_methods basically
# but ufunc may pass integer to _add_delta
return other
# raise when input doesn't have freq
msg = "Input has different freq from {cls}(freq={freqstr})"
raise IncompatibleFrequency(msg.format(cls=type(self).__name__,
freqstr=self.freqstr))
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill:
if isna(fill_value):
fill_value = iNaT
elif isinstance(fill_value, Period):
if self.freq != fill_value.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freq.freqstr,
fill_value.freqstr)
raise IncompatibleFrequency(msg)
fill_value = fill_value.ordinal
else:
msg = "'fill_value' should be a Period. Got '{}'."
raise ValueError(msg.format(fill_value))
new_values = algos.take(self._data,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(new_values, self.freq)
def take(self, indices, allow_fill=False, fill_value=None):
if allow_fill:
if isna(fill_value):
fill_value = iNaT
elif isinstance(fill_value, Period):
if self.freq != fill_value.freq:
msg = DIFFERENT_FREQ_INDEX.format(
self.freq.freqstr,
fill_value.freqstr
)
raise IncompatibleFrequency(msg)
fill_value = fill_value.ordinal
else:
msg = "'fill_value' should be a Period. Got '{}'."
raise ValueError(msg.format(fill_value))
new_values = algos.take(self._data,
indices,
allow_fill=allow_fill,
fill_value=fill_value)
return type(self)(new_values, self.freq)
def __init__(self, values, freq=None, copy=False):
if freq is not None:
freq = Period._maybe_convert_freq(freq)
if isinstance(values, ABCSeries):
values = values._values
if not isinstance(values, type(self)):
raise TypeError("Incorrect dtype")
elif isinstance(values, ABCPeriodIndex):
values = values._values
if isinstance(values, type(self)):
if freq is not None and freq != values.freq:
msg = DIFFERENT_FREQ_INDEX.format(values.freq.freqstr,
freq.freqstr)
raise IncompatibleFrequency(msg)
values, freq = values._data, values.freq
values = np.array(values, dtype='int64', copy=copy)
self._data = values
if freq is None:
raise ValueError('freq is not specified and cannot be inferred')
self._dtype = PeriodDtype(freq)
def _sub_period_array(self, other):
"""
Subtract a Period Array/Index from self. This is only valid if self
is itself a Period Array/Index, raises otherwise. Both objects must
have the same frequency.
Parameters
----------
other : PeriodIndex or PeriodArray
Returns
-------
result : np.ndarray[object]
Array of DateOffset objects; nulls represented by NaT
"""
if not is_period_dtype(self):
raise TypeError("cannot subtract {dtype}-dtype from {cls}".format(
dtype=other.dtype, cls=type(self).__name__))
if len(self) != len(other):
raise ValueError("cannot subtract arrays/indices of "
"unequal length")
if self.freq != other.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
new_values = checked_add_with_arr(self.asi8,
-other.asi8,
arr_mask=self._isnan,
b_mask=other._isnan)
new_values = np.array([self.freq * x for x in new_values])
if self.hasnans or other.hasnans:
mask = (self._isnan) | (other._isnan)
new_values[mask] = NaT
return new_values
def _sub_period_array(self, other):
"""
Subtract a Period Array/Index from self. This is only valid if self
is itself a Period Array/Index, raises otherwise. Both objects must
have the same frequency.
Parameters
----------
other : PeriodIndex or PeriodArray
Returns
-------
result : np.ndarray[object]
Array of DateOffset objects; nulls represented by NaT
"""
if not is_period_dtype(self):
raise TypeError("cannot subtract {dtype}-dtype to {cls}"
.format(dtype=other.dtype,
cls=type(self).__name__))
if not len(self) == len(other):
raise ValueError("cannot subtract arrays/indices of "
"unequal length")
if self.freq != other.freq:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
new_values = checked_add_with_arr(self.asi8, -other.asi8,
arr_mask=self._isnan,
b_mask=other._isnan)
new_values = np.array([self.freq * x for x in new_values])
if self.hasnans or other.hasnans:
mask = (self._isnan) | (other._isnan)
new_values[mask] = NaT
return new_values
def _check_compatible_with(self, other):
if self.freqstr != other.freqstr:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
def _check_compatible_with(self, other):
if self.freqstr != other.freqstr:
msg = DIFFERENT_FREQ_INDEX.format(self.freqstr, other.freqstr)
raise IncompatibleFrequency(msg)
